Ford Global Technolgoies, LLC

Argo Ai Gmbh

Siemens Mobility Gmbh

Francesco Ferroni

Guy Hotson

Nicolas Cebron

Felix Friedmann

Kiwoo Shin

Harpreet Banvait

Andrii Iegorov

Nirav Golakiya

A method for operating a processing unit of a vehicle for processing sensor data of several different sensors with an artificial neural network, wherein a set of volume data cells is provided as a volumetric representation of different volume elements of an environment, and when sensor data is generated by the sensors the sensor data is transferred to the respective volume data cells using an inverse mapping function, wherein each inverse mapping function is a mapping of a respective sensor coordinate system of the sensor to an internal volumetric coordinate system corresponding to the world coordinate system, and by the transfer of the sensor data each volume data cell receives the sensor data that are associated with this volume data cell according to the inverse mapping function from each sensor, wherein the received sensor data from each sensor are accumulated in the respective volume data cell as combined data.

Method and processing unit for processing sensor data of several different sensors with an artificial neural network in a vehicle

A system will generate a vector map of a geographic area using a method that includes receiving a birds-eye view image of a geographic area. The birds-eye view image comprises various pixels. The system will process the birds-eye view image to generate a spatial graph representation of the geographic area, and it will save the node pixels and the lines to a vector map data set. The processor may be a component of a vehicle such as an autonomous vehicle. If so, the system may use the vector map data set to generate a trajectory for the vehicle as the vehicle moves in the geographic area.

Automatic generation of vector map for vehicle navigation

Systems and methods for image-based perception. The methods comprise: capturing images by a plurality of cameras with overlapping fields of view; generating, by a computing device, spatial feature maps indicating locations of features in the images; identifying, by the computing device, overlapping portions of the spatial feature maps; generating, by the computing device, at least one combined spatial feature map by combining the overlapping portions of the spatial feature maps together; and/or using, by the computing device, the at least one combined spatial feature map to define a predicted cuboid for at least one object in the images.

Systems and methods for image based perception

A relative position of an object in the surroundings of a vehicle is estimated based on a two-dimensional camera image. A control unit determines an object contour of the object from the camera image and determines at least one digital object template that represents the object based on the object contour. The control unit forward projects the at least one object template from respective different positions onto an image plane of the camera image. Each forward-projected object template yields a respective two-dimensional contour proposal, and the control unit compares the contour proposals with the object contour of the object.

Method for estimating a relative position of an object in the surroundings of a vehicle and electronic control unit for a vehicle and vehicle

Systems and methods for image-based perception. The methods comprise: obtaining, by a computing device, images captured by a plurality of cameras with overlapping fields of view; generating, by the computing device, spatial feature maps indicating locations of features in the images; defining, by the computing device, predicted cuboids at each location of an object in the images based on the spatial feature maps; and assigning, by the computing device, at least two cuboids of said predicted cuboids to a given object when predictions from images captured by separate cameras of the plurality of cameras should be associated with a same detected object.

A method recognizes an object of a mobile unit in a digital image that shows at least one partition of the mobile unit, especially in motion, by using a method for machine learning. To provide an accurate and reliable recognition the method includes using machine learning in a categorization step for categorizing the digital image, which shows the partition of the mobile unit, with a category. By using the machine learning in a detection step the object of the mobile unit in the categorized digital image and a location of the object in the categorized digital image are determined. By using machine learning in a segmentation step positions in the categorized digital image are classified such that it is determined whether at a respective position of the categorized digital image a part of the object is present or not.

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Bayern, Germany

Francesco Ferroni